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One of the most intriguing questions in evolution is how organisms exhibitsuitable phenotypic variation to rapidly adapt in novel selective environmentswhich is crucial for evolvability. Recent work showed that when selectiveenvironments vary in a systematic manner, it is possible that development canconstrain the phenotypic space in regions that are evolutionarily moreadvantageous. Yet, the underlying mechanism that enables the spontaneousemergence of such adaptive developmental constraints is poorly understood. Howcan natural selection, given its myopic and conservative nature, favourdevelopmental organisations that facilitate adaptive evolution in futurepreviously unseen environments? Such capacity suggests a form of\textit{foresight} facilitated by the ability of evolution to accumulate andexploit information not only about the particular phenotypes selected in thepast, but regularities in the environment that are also relevant to futureenvironments. Here we argue that the ability of evolution to discover suchregularities is analogous to the ability of learning systems to generalise frompast experience. Conversely, the canalisation of evolved developmentalprocesses to past selective environments and failure of natural selection toenhance evolvability in future selective environments is directly analogous tothe problem of over-fitting and failure to generalise in machine learning. Weshow that this analogy arises from an underlying mechanistic equivalence byshowing that conditions corresponding to those that alleviate over-fitting inmachine learning enhance the evolution of generalised developmentalorganisations under natural selection. This equivalence provides access to awell-developed theoretical framework that enables us to characterise theconditions where natural selection will find general rather than particularsolutions to environmental conditions.

How Evolution Learns to Generalise: Principles of under-fitting, over-fitting and induction in the evolution of developmental organisation